SciPy 0.10.0 is the culmination of 8 months of hard work. It contains
many new features, numerous bug-fixes, improved test coverage and
better documentation. There have been a limited number of deprecations
and backwards-incompatible changes in this release, which are documented
below. All users are encouraged to upgrade to this release, as there
are a large number of bug-fixes and optimizations. Moreover, our
development attention will now shift to bug-fix releases on the 0.10.x
branch, and on adding new features on the development master branch.

Release highlights:

Support for Bento as optional build system.

Support for generalized eigenvalue problems, and all shift-invert modes
available in ARPACK.

This release requires Python 2.4-2.7 or 3.1- and NumPy 1.5 or greater.

Scipy can now be built with Bento.
Bento has some nice features like parallel builds and partial rebuilds, that
are not possible with the default build system (distutils). For usage
instructions see BENTO_BUILD.txt in the scipy top-level directory.

Currently Scipy has three build systems, distutils, numscons and bento.
Numscons is deprecated and is planned and will likely be removed in the next
release.

Support for simulating discrete-time linear systems, including
scipy.signal.dlsim, scipy.signal.dimpulse, and scipy.signal.dstep,
has been added to SciPy. Conversion of linear systems from continuous-time to
discrete-time representations is also present via the
scipy.signal.cont2discrete function.

A Lomb-Scargle periodogram can now be computed with the new function
scipy.signal.lombscargle.

The forward-backward filter function scipy.signal.filtfilt can now
filter the data in a given axis of an n-dimensional numpy array.
(Previously it only handled a 1-dimensional array.) Options have been
added to allow more control over how the data is extended before filtering.

FIR filter design with scipy.signal.firwin2 now has options to create
filters of type III (zero at zero and Nyquist frequencies) and IV (zero at zero
frequency).

The one-sided form of Fisher’s exact test is now also implemented in
stats.fisher_exact.

The function stats.chi2_contingency for computing the chi-square test of
independence of factors in a contingency table has been added, along with
the related utility functions stats.contingency.margins and
stats.contingency.expected_freq.

The maxentropy module is unmaintained, rarely used and has not been functioning
well for several releases. Therefore it has been deprecated for this release,
and will be removed for scipy 0.11. Logistic regression in scikits.learn is a
good alternative for this functionality. The scipy.maxentropy.logsumexp
function has been moved to scipy.misc.